Comparison of Oil Spill Classifications Using Fully and Compact Polarimetric SAR Images
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TLDR
In this article, a comparison between several algorithms for oil spill classifications using fully and compact polarimetric satellite synthetic aperture radar (SAR) images is presented, where dimension reduction algorithms, including principle component analysis (PCA) and the local linear embedding (LLE) algorithm, are employed to learn low dimensional and distinctive information from quad-polarimetric SAR features.Abstract:
In this paper, we present a comparison between several algorithms for oil spill classifications using fully and compact polarimetric SAR images. Oil spill is considered as one of the most significant sources of marine pollution. As a major difficulty of SAR-based oil spill detection algorithms is the classification between mineral and biogenic oil, we focus on quantitatively analyzing and comparing fully and compact polarimetric satellite synthetic aperture radar (SAR) modes to detect hydrocarbon slicks over the sea surface, discriminating them from weak-damping surfactants, such as biogenic slicks. The experiment was conducted on quad-pol SAR data acquired during the Norwegian oil-on-water experiment in 2011. A universal procedure was used to extract the features from quad-, dual- and compact polarimetric SAR modes to rank different polarimetric SAR modes and common supervised classifiers. Among all the dual- and compact polarimetric SAR modes, the π/2 mode has the best performance. The best supervised classifiers vary and depended on whether sufficient polarimetric information can be obtained in each polarimetric mode. We also analyzed the influence of the number of polarimetric parameters considered as inputs for the supervised classifiers, onto the detection/discrimination performance. We discovered that a feature set with four features is sufficient for most polarimetric feature-based oil spill classifications. Moreover, dimension reduction algorithms, including principle component analysis (PCA) and the local linear embedding (LLE) algorithm, were employed to learn low dimensional and distinctive information from quad-polarimetric SAR features. The performance of the new feature sets has comparable performance in oil spill classification.read more
Citations
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Journal ArticleDOI
Improved Classification Models to Distinguish Natural from Anthropic Oil Slicks in the Gulf of Mexico: Seasonality and Radarsat-2 Beam Mode Effects under a Machine Learning Approach
Italo de Oliveira Matias,Patrícia Carneiro Genovez,Sarah Barrón Torres,Francisco Fabio de Araujo Ponte,Anderson José Silva de Oliveira,Fernando Pellon de Miranda,Gil Márcio Avellino +6 more
TL;DR: In this paper, a machine learning (ML) method was employed to develop, test, and implement a classification model (CM) to distinguish an oil slick source (OSS) as natural or anthropic.
Progress in the Processing and Application of Compact Polarimetric SAR
TL;DR: The classical data processing methods of CP SAR are briefly introduced, the main research results of the application ofCP SAR in the agriculture and maritime fields over the past 10 years are summarized and the prospects on its development are given.
Peer ReviewDOI
Compact Polarimetric Synthetic Aperture Radar for Target Detection: A review
TL;DR: In this paper , the authors provide a review of compact polarimetric (CP) SAR target detection methods and present three promising research directions, i.e., the further deepening of feature extraction, deeper understanding of target characteristics, and development of intelligent detection techniques.
Spatial Domain Generation of Random Surface Using Savitzky-Golay Filter for Simulation of Electromagnetic Polarimetric Systems
TL;DR: In this paper, a spatial-domain algorithm is introduced for generating both isotropic and anisotropic natural rough surface models with predetermined statistical properties using Savitzky-Golay filter.
Dissertation
Analysis of Oil Spill and Sea Ice Measurements Using Full-Polarimetric and Hybrid-Polarity Synthetic Aperture Radar data
TL;DR: Paper 2 and Paper 3 as mentioned in this paper er publisert enda, men sendt inn til en journal til review for review, men skal dermed ikke publiseres online pa munin-siden hvis oppgaven blir godkjent.
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